




'''simple docstring'''







import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None						):






   '''simple docstring'''







   assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
   A__       =     nn.Parameter(UpperCAmelCase						)
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
      A__       =     nn.Parameter(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     np.asarray(weights[0]						)
   A__       =     np.asarray(weights[1]						)
   A__       =     np.asarray(weights[2]						)

   set_param(
       torch_layer.self_attention.query_key      ,torch.tensor(UpperCAmelCase						).transpose(1      ,2						).contiguous().view(-1      ,UpperCAmelCase						)      ,)
   set_param(
       torch_layer.self_attention.value      ,torch.tensor(UpperCAmelCase						).transpose(1      ,2						).contiguous().view(-1      ,UpperCAmelCase						)      ,)
   set_param(
       torch_layer.output.dense      ,torch.tensor(UpperCAmelCase						).view(-1      ,UpperCAmelCase						).contiguous().transpose(0      ,1						)      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     np.asarray(weights[0]						)
   A__       =     np.asarray(weights[1]						)
   A__       =     np.asarray(weights[2]						)
   A__       =     np.asarray(weights[3]						)

   set_param(
       torch_layer.self_attention.query      ,torch.tensor(UpperCAmelCase						).transpose(1      ,2						).contiguous().view(-1      ,UpperCAmelCase						)      ,)
   set_param(
       torch_layer.self_attention.key      ,torch.tensor(UpperCAmelCase						).transpose(1      ,2						).contiguous().view(-1      ,UpperCAmelCase						)      ,)
   set_param(
       torch_layer.self_attention.value      ,torch.tensor(UpperCAmelCase						).transpose(1      ,2						).contiguous().view(-1      ,UpperCAmelCase						)      ,)
   set_param(
       torch_layer.output.dense      ,torch.tensor(UpperCAmelCase						).view(-1      ,UpperCAmelCase						).contiguous().transpose(0      ,1						)      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     weights[0][0][0]
   A__       =     np.asarray(layer_norm_a[0]						)
   A__       =     np.asarray(layer_norm_a[1]						)
   set_param(
       torch_block.attention.layer_norm      ,torch.tensor(UpperCAmelCase						)      ,torch.tensor(UpperCAmelCase						)      ,)

   # lsh weights + output
   A__       =     weights[0][1]
   if len(UpperCAmelCase						) < 4:
      set_layer_weights_in_torch_lsh(UpperCAmelCase      ,torch_block.attention      ,UpperCAmelCase						)
   else:
      set_layer_weights_in_torch_local(UpperCAmelCase      ,torch_block.attention      ,UpperCAmelCase						)

   # intermediate weighs
   A__       =     weights[2][0][1][2]

   # Chunked Feed Forward
   if len(UpperCAmelCase						) == 4:
      A__       =     intermediate_weights[2]

   # layernorm 2
   A__       =     np.asarray(intermediate_weights[0][0]						)
   A__       =     np.asarray(intermediate_weights[0][1]						)
   set_param(
       torch_block.feed_forward.layer_norm      ,torch.tensor(UpperCAmelCase						)      ,torch.tensor(UpperCAmelCase						)      ,)

   # intermediate dense
   A__       =     np.asarray(intermediate_weights[1][0]						)
   A__       =     np.asarray(intermediate_weights[1][1]						)
   set_param(
       torch_block.feed_forward.dense.dense      ,torch.tensor(UpperCAmelCase						).transpose(0      ,1						).contiguous()      ,torch.tensor(UpperCAmelCase						)      ,)

   # intermediate out
   A__       =     np.asarray(intermediate_weights[4][0]						)
   A__       =     np.asarray(intermediate_weights[4][1]						)
   set_param(
       torch_block.feed_forward.output.dense      ,torch.tensor(UpperCAmelCase						).transpose(0      ,1						).contiguous()      ,torch.tensor(UpperCAmelCase						)      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     torch_model.reformer

   # word embeds
   A__       =     np.asarray(weights[1]						)
   set_param(
       torch_model_reformer.embeddings.word_embeddings      ,torch.tensor(UpperCAmelCase						)      ,)

   if isinstance(weights[3]      ,UpperCAmelCase						):
      A__       =     torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights						)						):
         A__       =     np.asarray(weights[3][emb_idx][0]						)
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), F"""{position_embeddings[emb_idx]} emb does not match"""
         A__       =     nn.Parameter(torch.tensor(UpperCAmelCase						)						)

   A__       =     weights[5]
   assert len(torch_model_reformer.encoder.layers						) * 4 == len(
       UpperCAmelCase						), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
      A__       =     trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # output layer norm
   A__       =     np.asarray(weights[7][0]						)
   A__       =     np.asarray(weights[7][1]						)
   set_param(
       torch_model_reformer.encoder.layer_norm      ,torch.tensor(UpperCAmelCase						)      ,torch.tensor(UpperCAmelCase						)      ,)

   # output embeddings
   A__       =     np.asarray(weights[9][0]						)
   A__       =     np.asarray(weights[9][1]						)
   set_param(
       torch_model.lm_head.decoder      ,torch.tensor(UpperCAmelCase						).transpose(0      ,1						).contiguous()      ,torch.tensor(UpperCAmelCase						)      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     ReformerConfig.from_json_file(UpperCAmelCase						)
   print(F"""Building PyTorch model from configuration: {config}"""						)
   A__       =     ReformerModelWithLMHead(UpperCAmelCase						)

   with open(UpperCAmelCase      ,'rb'						) as f:
      A__       =     pickle.load(UpperCAmelCase						)['weights']

   set_model_weights_in_torch(UpperCAmelCase      ,UpperCAmelCase      ,config.hidden_size						)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
   torch.save(model.state_dict()      ,UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained Reformer model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    lowerCAmelCase_     						=				parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''







import argparse
import copy







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   with open(UpperCAmelCase						) as f:
      for line in f:
         if line.split()[0] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[1], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[0]].append(
                [line.split()[1], line.split()[2]]						)
         if line.split()[1] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[0], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[1]].append(
                [line.split()[0], line.split()[2]]						)

   return dict_of_neighbours







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      A__       =     f.read(1						)
   A__       =     start_node

   A__       =     []

   A__       =     start_node

   A__       =     0
   while visiting not in first_solution:
      A__       =     10000
      for k in dict_of_neighbours[visiting]:
         if int(k[1]						) < int(UpperCAmelCase						) and k[0] not in first_solution:
            A__       =     k[1]
            A__       =     k[0]

      first_solution.append(UpperCAmelCase						)
      A__       =     distance_of_first_solution + int(UpperCAmelCase						)
      A__       =     best_node

   first_solution.append(UpperCAmelCase						)

   A__       =     0
   for k in dict_of_neighbours[first_solution[-2]]:
      if k[0] == start_node:
         break
      position += 1

   A__       =     (
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 10000
   )
   return first_solution, distance_of_first_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for n in solution[1:-1]:
      A__       =     solution.index(UpperCAmelCase						)
      for kn in solution[1:-1]:
         A__       =     solution.index(UpperCAmelCase						)
         if n == kn:
            continue

         A__       =     copy.deepcopy(UpperCAmelCase						)
         A__       =     kn
         A__       =     n

         A__       =     0

         for k in _tmp[:-1]:
            A__       =     _tmp[_tmp.index(UpperCAmelCase						) + 1]
            for i in dict_of_neighbours[k]:
               if i[0] == next_node:
                  A__       =     distance + int(i[1]						)
         _tmp.append(UpperCAmelCase						)

         if _tmp not in neighborhood_of_solution:
            neighborhood_of_solution.append(_tmp						)

   A__       =     len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda UpperCAmelCase						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     1
   A__       =     first_solution
   A__       =     []
   A__       =     distance_of_first_solution
   A__       =     solution

   while count <= iters:
      A__       =     find_neighborhood(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     0
      A__       =     neighborhood[index_of_best_solution]
      A__       =     len(UpperCAmelCase						) - 1

      A__       =     False
      while not found:
         A__       =     0
         while i < len(UpperCAmelCase						):
            if best_solution[i] != solution[i]:
               A__       =     best_solution[i]
               A__       =     solution[i]
               break
            A__       =     i + 1

         if [first_exchange_node, second_exchange_node] not in tabu_list and [
             second_exchange_node,
             first_exchange_node,
         ] not in tabu_list:
            tabu_list.append([first_exchange_node, second_exchange_node]						)
            A__       =     True
            A__       =     best_solution[:-1]
            A__       =     neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               A__       =     cost
               A__       =     solution
         else:
            A__       =     index_of_best_solution + 1
            A__       =     neighborhood[index_of_best_solution]

      if len(UpperCAmelCase						) >= size:
         tabu_list.pop(0						)

      A__       =     count + 1

   return best_solution_ever, best_cost







def 						_A      ( UpperCAmelCase=None						):






   '''simple docstring'''







   A__       =     generate_neighbours(args.File						)

   A__  ,			A__       =     generate_first_solution(
       args.File      ,UpperCAmelCase						)

   A__  ,			A__       =     tabu_search(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,args.Iterations      ,args.Size      ,)

   print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''Tabu Search''')
    parser.add_argument(
        '''-f''',
        '''--File''',
        type=str,
        help='''Path to the file containing the data''',
        required=True,
    )
    parser.add_argument(
        '''-i''',
        '''--Iterations''',
        type=int,
        help='''How many iterations the algorithm should perform''',
        required=True,
    )
    parser.add_argument(
        '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


